Temporal neural dynamics patterns in episodic and chronic migraine: a magnetoencephalography study
Abstract
Background: Chronic migraine (CM) severely affects patients' work and daily life, imposing a significant economic burden. However, the underlying neural mechanisms of migraine chronification remain unclear. This study aimed to characterize temporal neural dynamics patterns of migraine via magnetoencephalography (MEG) combined with dynamic network mode (DyNeMo), providing further neuroimaging evidence for migraine chronification.
Methods: This cross-sectional study recruited patients with episodic migraine (EM), CM, and healthy controls (HC). MEG data were acquired during resting and somatosensory stimulation states. The DyNeMo model was applied to source-reconstructed MEG data to quantify temporal neural dynamics metrics, including mean lifetime, mean interval, switching rate and fractional occupancy. Permutation-based analysis of covariance (ANCOVA) with Bonferroni correction and Spearman correlation with false discovery rate (FDR) correction were applied.
Results: Six distinct brain modes were identified: visual network (VN), anterior and posterior default mode networks (aDMN/pDMN), right and left sensorimotor networks (rSMN/lSMN), and auditory network (AN). During resting state, EM showed prolonged mean lifetime and decreased switching rate of the VN vs. HC; CM showed increased switching rate of the VN vs. EM; CM showed prolonged mean lifetime and decreased switching rate of the AN vs. HC. During somatosensory stimulation state, both EM and CM showed prolonged mean…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
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